Wireless Synchronized Movement Monitoring Apparatus and System

ABSTRACT

Disclosed embodiments include a movement monitoring apparatus comprising a wireless synchronization scheme. Depending on the particular embodiment such wireless synchronization scheme is a master synchronization scheme or a mesh synchronization scheme. Additionally, in a particular embodiment, the movement monitor further comprises a robust wireless data transfer data controller. The disclosure includes a description of the complete system, namely, the wireless synchronized movement monitors with robust data transfer capabilities, the docking station, the access point, and the computer-implemented analysis system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-In-Part of U.S. patent applicationSer. No. 12/632,778 filed on 2009 Dec. 7, which claims the benefit of USProvisional Application No. 61120485 filed on 2008 Dec. 7, and arehereby incorporated by reference in their entirety. This applicationalso claims the benefit of US Provisional Application No. 61308764 filedon 2010 Feb. 26, which is incorporated herein by reference in itsentirety.

TECHNICAL FIELD

Disclosed embodiments relate to the physiologic monitoring of movement.Specifically, they relate to systems and devices for continuous andambulatory measurement of the symptoms of movement disorders usingwearable monitoring devices.

BACKGROUND A. Movement Monitors

State of the art movement disorder monitors employ inertial sensors,such as accelerometers and gyroscopes, to measure position, velocity andacceleration of the subject's limbs and trunk. Current monitors fallinto two classes, namely activity monitors and inertial monitors, bothof which have disadvantages and limitations that make them incapable ofcontinuous monitoring of movement disorders in ambulatory settings.

Activity monitors, such as in U.S. Pat. No. 4,353,375, collect lowfrequency and low resolution samples of the subject's gross activity fordays to weeks at a time. These monitors are usually small, unobtrusivedevices resembling watches or brooches which are worn by the subject forlong periods of time such as days or weeks outside of the clinicalsetting. They measure movement using low quality inertial sensors at lowsampling frequencies, and usually measure only a few degrees of freedomof motion instead of all six possible degrees of freedom of motion. Thelow quality measurements are stored in data storage on-board the devicewhich is later downloaded and analyzed. While they are useful forrecording the gross activity levels of the subject, and they may becomfortable and unobtrusive enough to be worn by the subject for longsperiods of time, they are only useful in measuring non-subtle symptomsof movement disorders such as activity versus rest cycles. Subtlesymptoms, such as symptom onset and decline, or non-obvious symptomssuch as bradykinesia, can not be measured by these devices. Thesedevices, also known as actigraphers, typically measure movement countsper minute which make even simple determinations such as determining thewake-up time challenging. Consequently, actigraphers are inappropriatefor continuous ambulatory monitoring of movement disorders such as inParkinson's disease.

Inertial monitors, such as in U.S. Pat. No. 5,293,879, collect highfrequency, high resolution samples of the subject's movements for shortperiods of time. These devices are larger and more obtrusive, resemblingsmall boxes which are worn by the subject for short periods of time suchas hours, or at most, a day, and usually in clinical settings. Theymeasure movement using high quality inertial sensors, and usuallyinclude all six degrees of freedom of motion (three linear axes andthree rotational axes). Inertial monitors may store the inertialmeasurements in the device for later analysis, or they may use telemetryradios to wirelessly transmit the measurements in real-time to a nearbycomputer or recording device. These devices are useful for measuring allsymptoms of movement disorders, but because of their larger, obtrusivesize and short operational times, they are not useful for measuringsymptoms outside of clinical settings or for long periods of time.

Movement disorder monitoring can be enhanced by monitoring multiplelocations on a subject at the same time. Current systems either do notsynchronize their measurements, or require wires to synchronizesampling. Additionally, current movement disorder monitoring devicesalso lack aiding sensors, such as absolute measures of position.

Movement monitoring devices and systems that overcome challenges ofphysical size, power consumption, and wireless synchronization arecurrently unavailable and have significant potential in numerousapplications including clinical practice and research.

Currently, the most common and accurate method of tracking movement isbased on optical motion analysis systems. However, these systems areexpensive, can only measure movements in a restricted laboratory space,and cannot be used to observe patients at home.

Current inertial monitoring systems can be divided into threecategories: computer-tethered, unit-tethered, and untethered.Computer-tethered devices connect the sensor directly to a computer. Oneof the best systems in this category is MotionNode (GLI Interactive LLC,Seattle). These systems are not practical for home settings.Unit-tethered systems connect the sensors to a central recording unitthat is typically worn around the waist. This unit typically houses thememory, batteries, and wireless communications circuits. Currently,these systems are the most widely available and are the most common inprevious studies. One of the best systems in this category is the Xbuskit (Xsens, Netherlands). This system includes up to five sensors, eachwith high-performance, triaxial accelerometers, gyroscopes, andmagnetometers. The system can operate continuously and wirelessly streamdata via Bluetooth to a laptop for over 3 h at distances up to 100 m.However the system is too cumbersome and difficult to use in a homestudy due to the wires connecting the sensors and central recordingunit, the battery life is too short, and the interconnecting wires maybe hazardous during normal daily activities. The typical untetheredsystem combines the batteries, memory, and sensors in single stand-aloneunits. The only wireless untethered systems reported in the literatureare “activity monitors,” which measure the coarse degree of activity atintervals of 1-60 s, typically with a wrist-worn device that contains asingle-axis accelerometer. These devices are sometimes called actigraphsor actometers. Most of these devices only report activity counts, whichare a measure of how frequently the acceleration exceeds a threshold.Some custom activity monitors directly compute specific metrics of motorimpairment, such as tremor. A few studies have shown that activitymonitors worn over 5-10 days could detect on/off fluctuations, decreasedactivity from hypokinesia, and increased activity associated withdyskinesia. However, typical activity monitors cannot distinguishbetween motor activity caused by voluntary movement, tremor, ordyskinesia. They do not have sufficient bandwidth, memory, or sensorsfor precise monitoring of motor impairment in PD. They also cannotdistinguish between periods of hypokinesia and naps.

Recently, Cleveland Medical Devices (Cleveland, Ohio) introduced twountethered systems, the KinetiSense and Kinesia devices. These systemsinclude triaxial accelerometers and gyroscopes with bandwidths of 0-15Hz, but lack magnetometers. Although large, the central recording unitscould to be worn on the wrist. The sensor and recording unit can beconnected to form a single unit. This devices can record datacontinuously and store it on an on-board memory for up to 12 h.However, 1) the due to their size it is difficult for several of thesedevices to be used at the same time (e.g. wrist, ankle, waits, trunk),2) the storage capability is limited to a single day and consequently itis difficult to conduct multiple day studies, and 3) the devices are notsynchronized.

Movement monitoring devices and systems that overcome the challengesof 1) physical size (volume), 2) power consumption, 3) wirelesssynchronization, 4) wireless connectivity, 5) automatic calibration, and6) noise floor; are currently unavailable and have significant potentialin numerous applications including clinical practice and research.Finally, the limited solutions currently available are device-centricand do not include a complete platform to perform collection,monitoring, uploading, analysis, and reporting.

B. Wireless Synchronization

While there are several commercial movement monitors available capableof wireless data transmission, currently none of these movement monitorsis capable of providing wireless synchronization of the samplinginstances. The most advanced inertial monitors capable of wireless datatransfer such as Xsens' full body motion capture monitor (XSensTechnologies) require wires between each of the movement monitors and acentral unit in order to synchronize the sampling instances of each ofthe monitors. Synchronization is critical for applications where morethan one movement monitor is needed.

Wireless sensor networks have multiple independent nodes all sensingenvironmental factors at the same time. In the case of a wearablewireless movement monitor, these environmental factors are the kineticstate of the various limbs of a subject wearing two or more movementmonitors. Later, during data analysis, the samples of the two or moremovement monitors must correlated in time to make any sense together.For example, two movement monitors on the ankles need to be correlatedin time in order to show the difference between a lopsided gallop and asmooth run. The problem is that in order to be correlated in time, thesensors must sample at the same time, and, over time, at the same rate,over a long time period of hours, or even days.

There are many ways to do this correlation, but the challenge with smallwireless sensor systems is how to go about providing thissynchronization of the sampling time and rate without unduly impactingother system parameters.

One way in which current wireless sensor networks synchronize with eachother is to provide a wired sync line between nodes. While simple andeffective, this not only provides annoying wires running between nodes,but obviously defeats the wireless part of the wireless sensor network.

Another way wireless sensors synchronize their sampling time and ratesis by attempting to post-process the data to correlate common events intime. The problem is that disparate sensor locations can sometimes havevery little data in common, and many times there is not enoughinformation in common to quickly and reliably correlate the data. Forexample, a movement monitor on the right wrist and left ankle usuallyhave very little kinetic information in common.

Another way that post processing can be done is by purposely injecting asignal into all sensors at the same time. For movement monitors, thisrequires the subject to do a sudden, rapid motion at regular intervals,like a jump or a fall. This rapidly becomes annoying to the subject, andproduces unreliable synchronization information, especially if thesubject does not perform the synchronization move correctly becausethey're tired—or even asleep.

Another synchronization method for wireless sensor networks is to startthe sampling at a known time when the units are together, and then relyon a high precision timing source in each node, such as a temperaturecompensated crystal oscillator, to keep the units synchronized. This hasthe disadvantage that such high precision timing sources are usuallylarge and consume much more power—sometimes as much as ten times thepower—as regular timing components. Further, despite the significantreduction in the timing drift using high precision timing components,drift is not eliminated, and over long timer periods, like days, thesedevices do drift. Worse, if the various components experience differenttemperatures (such as one motion monitor on the sternum under a jacketand one exposed to the elements on a wrist), then the drift is muchworse.

C. Robust Wireless Data Transfer

In small, highly mobile wireless devices, such as wireless movementmonitors, it is necessary to robustly stream large amounts of data (100s of bits to 100 s of kilobits per second) in near real time (withoutlarge latencies in transmission) over a radio frequency communicationchannel. These continuous, real-time wireless transmissions often sufferfrom unpredictable data loss due to a variety of environmental factors,including distance between transmitter and receiver, absorption of thesignals by local materials (including human bodies), multipathinterference due to objects which reflect or refract signals, and eveninterference from other devices. The challenge with these small embeddedsystems is how to go about guaranteeing transmission of the signalwithout unduly impacting other system parameters.

One way in which current wireless movement monitors overcometransmission problems, such as distance and interference, is to increasethe radio frequency (RF) signal strength of their transmissions and/orto use receive amplifiers. Either method leads to an large increase inconsumed power, which leads to larger battery sizes, which leads todramatically larger and heavier devices, forcing some systems to evenhave large, separate wired unit which holds a replaceable battery pack.

Another way in which current wireless sensors overcome radio problems isby using a high gain antenna. The tradeoff here is that the high gainantenna means large size, so that the antenna size alone can equal thesize of the wireless sensor.

A third way these wireless systems overcome radio problems is by usingstate-of-the-art transmission protocols and encodings. The problem withthese systems is that the increased complexity of the radio encoding orprotocol requires large RF chipsets and increased power consumption,both of which negatively impact size and weight.

A fourth way to overcome radio transmission issues is by having a localdata buffer on-board the sensor, which allows later re-transmission ofthe data packet when the transmission issue has been solved (that is,the interference is over or the transmission distance has been reduced).The problem here is that small embedded devices usually employ amicrocontroller that has small amounts of RAM (usually 10 s to 100 s ofkilobytes) which allows buffering of only a few seconds of data beforethe buffers overflow.

None of these ways to overcome radio communication disruptions allows awireless sensor to remain small, reduce power consumption, and avoiddata loss during long interruptions in communication.

SUMMARY

Disclosed embodiments include a movement monitoring apparatus comprisinga wireless synchronization scheme. Depending on the particularembodiment such wireless synchronization scheme is a mastersynchronization scheme or a mesh synchronization scheme. Additionally,in a particular embodiment, the movement monitor further comprises arobust wireless data transfer data controller.

BRIEF DESCRIPTION OF THE DRAWINGS

Disclosed embodiments are illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings:

FIG. 1 illustrates a block diagram representing the basic components ofan embodiment of the general systems for continuous and objectivemovement monitoring.

FIG. 2 illustrates a detailed diagram of the basic components andinterconnections of an embodiment of the wearable apparatus forcontinuous and objective movement monitoring.

FIG. 3 illustrates a block diagram representing an embodiment of awireless synchronization scheme based on a single master clock.

FIG. 4 illustrates a block diagram representing an embodiment of awireless synchronization scheme based on mesh synchronization.

FIG. 5 illustrates a block diagram representing an embodiment of awireless synchronization scheme based on mesh synchronization using theFlooding Time Synchronization Protocol (FTSP).

FIG. 6 illustrates a block diagram representing the basic components ofan embodiment of for robust wireless communications wireless systems.

FIG. 7 illustrates a particular embodiment of the movement monitor, thedocking station, and the docking mechanism.

FIG. 8 illustrates a second embodiment of the movement monitor, thedocking station, and the docking mechanism, this embodiment particularlyadapted to the wearable a wrist watch.

FIG. 9 illustrates embodiments of the movement monitor with sternum,waist, and wrist/ankle straps.

FIG. 10 illustrates an embodiment of the docking station and a connecteddocking station for simultaneously charging multiple movement monitors.

FIG. 11 illustrates an embodiment of the access point.

FIG. 12 illustrates a complete system comprise of a movement monitor, adocking station, and access point and a computer system for dataanalysis and visualization.

FIG. 13 illustrates an example comparing the use of the disclosedwireless synchronized wearable movement monitors and the closest relatedprior art.

FIG. 14 illustrates the use of the complete system according to oneembodiment where wireless mesh synchronized data is collected duringcontinuous ambulatory monitoring by the movement monitors and storedlocally until the monitors are docketed and the docking stationtransfers the data to a computer system including analysis methods tovisualize and produce reports of the results.

FIG. 15 illustrates the use of the complete system according to oneembodiment where wireless master or mesh synchronized data is collectedduring continuous monitoring by the movement monitors and stored locallyuntil the monitors are docketed and the docking station transfers thedata to a computer system including analysis methods to visualize andproduce reports of the results.

FIG. 16 illustrates the use of the complete system according to oneembodiment where wireless mesh synchronized data is collected duringcontinuous or objective monitoring by the movement monitors and suchdata is wirelessly streamed using robust wireless streaming to acomputer system including analysis methods to visualize and producereports of the results.

DETAILED DESCRIPTION A. Overall System Components

According to one embodiment, as shown in FIG. 1 the system forcontinuous ambulatory monitoring of movement disorders comprises: one ormore wearable devices (movement monitors) 100, one or more dockingstations 102 connected to a plurality of access points, one or more dataservers 104, and a plurality of statistical and signal processinganalysis methods 106 to process the movement data collected by thewearable devices and generate a plurality movement metrics. FIG. 12illustrates a complete system comprise of a movement monitor, a dockingstation, and access point and a computer system for data analysis andvisualization. FIG. 14-16 illustrates several uses of the completesystem.

B. Wearable Devices: Movement Monitors

According to one embodiment the wearable movement monitor 100 is alightweight device (<100 g) comprising (a) a sensor module comprising aplurality of low power (<50 mW) solid state and micro-electromechanicalsystems kinematics sensors; (b) a microprocessor module comprising a lowpower (<50 mW) microcontroller configured for device control, devicestatus, and device communication; (c) a data storage module comprising asolid state local storage medium; (d) a wireless communication modulecomprising a low power (<50 mW) surface mount transceiver and anintegrated antenna; and (e) a power and docking module comprising abattery, an energy charging regulator circuit, and a docking connector.In one embodiment, the micro-electromechanical systems kinematicssensors include a plurality of solid-state, surface mount, low power,low noise inertial sensors including a plurality of accelerometers andgyroscopes, as well as a solid-state, surface mount, low power, lownoise, Gigantic Magneto-Resistance (GMR) magnetometers. In a particularembodiment, the solid state local storage medium is substantiallyequivalent to a high capacity SD card (>4 GB) in order to enable formulti-day (>2 days) local storage of movement monitoring data at highfrequencies (sampling frequencies>20 Hz). In one embodiment, thecommunication module is designed to communicate with a plurality ofwearable movement monitors (peer-to-peer communication) in order tosynchronize the monitors, and to communicate with a host computer(peer-to-host communication) in order to transmit sensor data, uses abidirectional groundplane PCB patch antenna, and accepts transmissionsfrom a plurality of beacons to calculate the device location. In oneembodiment, the power and docking module includes an external connectorto access external power and provide high speed communication with anexternal docking station, the energy charging regulator circuit is asolid state integrated circuit charger such as a linear Lithium IonPolymer battery charger IC and said battery is a Lithium Ion Polymerbattery, and Lithium Ion Polymer battery can be selected for aparticular application as a function of its mAHr characteristics (e.g.450 mAHr or 50 mAHr).

According to another embodiment, the wearable movement monitoringapparatus 100 further comprises an external movement monitoring systemcomprising: (a) an external docking station for re-charging the wearablemovement monitoring apparatus, storing movement data, and transmittingthe movement data to a plurality of receiver devices, (b) a plurality ofwireless transceiver access points for wireless transmission of themovement data to a plurality of receiver devices, and (c) a web-enabledserver computer including a clinical data management and analysis systemfor storing, sharing, analyzing, and visualizing movement data using aplurality of statistical signal processing methods.

According to an embodiment the movement monitor apparatus 100 is alightweight, low-power, low noise, wireless wearable device with thefollowing characteristics: 1) weight of 22 g, 2) sampling frequency of128 Hz, 3) wireless synchronization, 4) 14 bit resolution, 5) three-axisMEMS accelerometers (user configurable from ±2 g to ±6 g), 6) three-axisMEMS gyroscopes with a ±1500 deg/s range, 7) three-axis magnetometerswith a ±6 Gauss range, 7) automatically calibrated, 8) over 16 hours ofoperation per charge, and 9) over 20 days of onboard storage capacity.According to an embodiment the device, and without limitation, thedevice 100 includes solid state, low-power, low-noise sensors asfollows: accelerometer (0.8 cm/s²/sqr(Hz)), XY gyroscope (0.05deg/s/sqrt(Hz)), z Gyroscope (0.05 deg/s/sqrt(Hz)), and magnetometer (40nT/sqrt(Hz)).

According to one embodiment, the wearable devices or apparatus 100 arecompact movement monitoring devices that continuously record data fromembedded sensors. The sensors 100 may be worn at any convenient locationon the body that can monitor impaired movement. Convenient locationsinclude the wrists, ankles, trunk, and waist. In one embodiment, thesensors include one or more channels of electromyography,accelerometers, gyroscopes, magnetometers, and other MEMS sensors thatcan be used to monitor movement. The wearable sensors 100 havesufficient memory and battery life to continuously record inertial datathroughout the day from the moment subjects wake up until they go tosleep at night, typically 18 hours or more. In one particular embodimentdesigned for continuous monitoring of movement during daily activitiesthe device uses a storage element substantially equivalent to an SD cardto store movement data for extended periods of time (e.g. 1 month). Thesensors 100 automatically start recording when they are removed from thedocking station. In one embodiment, there is no need for the user toturn them on or off.

According to one embodiment, the wearable devices 100 include thecomponents and interconnections detailed in FIG. 2: a sensor module 200,a microprocessor module 210, a data storage module 221, a wirelesscommunication module 230, and a power and docking module 243. Anembodiment of each of these modules comprising the apparatus forcontinuous and objective monitoring of movement disorders is describedin detail below. In addition to movement monitoring in clinicalapplications such as movement disorders, the embodiments disclosed canbe use to characterize movement in a plurality of application areasincluding continuous movement monitoring, activity monitoring,biomechanics, sports science, motion research, human movement analysis,orientation tracking, animation, virtual reality, ergonomics, andinertial guidance for navigation, robots and unmanned vehicles.

FIG. 8 illustrates a second embodiment of the movement monitor, thedocking station, and the docking mechanism, this embodiment particularlyadapted to the wearable a wrist watch. FIG. 9 illustrates embodiments ofthe movement monitor with sternum, waist, and wrist/ankle straps.

B.1. Sensor Module

The sensor module 200 in FIG. 2 contains the motion sensors necessary tocharacterize the symptoms of movement disorders. Three of these sensorsare low noise accelerometers 202. According to one embodiment, theaccelerometers are off-the-shelf, commercially availableMicro-ElectroMechanical Systems (MEMS) acceleration sensors in smallsurface-mount packages, such as the STMicro LIS344AHL. In otherembodiments, the acceleration sensors are custom made MEMSaccelerometers. The accelerometers are arranged in three orthogonal axeseither on a single multi-axis device, or by using one or more separatesensors in different mounting configurations. According to oneembodiment, the output of the accelerometers 202 is an analog signal.This analog signal needs to be filtered to remove high frequencycomponents by anti-aliasing filters 206, and then sampled by theanalog-to-digital (ADC) peripheral inputs of the microprocessor 212.According to one embodiment the anti-aliasing filters are single pole RClow-pass filters that require a high sampling frequency; in another,they are operational amplifiers with multiple-pole low pass filters thatmay use a slower sampling frequency. In other embodiments, the deviceincludes an analog interface circuit (AIC) with a programmableanti-aliasing filter. According to another embodiment, the output of theaccelerometers is digital, in which case the sensor must be configuredfor the correct gain and bandwidth and sampled at the appropriate rateto by the microprocessor 212.

The next three sensors in the sensor module 200 are solid state, lownoise rate gyroscopes 203. In one embodiment, the gryroscopes areoff-the-shelf, commercially available Micro-ElectroMechanical Systems(MEMS) rotational sensors in small surface-mount packages, such as a theInvensense IDG-650 and the Epson Toyocomm XV-3500CBY. In otherembodiments they are custom made MEMS. The gyroscopes are arranged inthree orthogonal axes either on a single multi-axis device, or by usingone or more separate sensors in different mounting configurations.According to one embodiment, the output of the gyroscopes 203 is ananalog signal. This analog signal needs to be filtered to remove highfrequency components by anti-aliasing filters 207, and then sampled bythe analog-to-digital (ADC) peripheral inputs of the microprocessor 212.According to one embodiment the anti-aliasing filters are single pole RClow-pass filters that require a high sampling frequency; in another,they are operational amplifiers with multiple-pole low pass filters thatmay use a slower sampling frequency. In other embodiments, the deviceincludes an analog interface circuit (AIC) with a programmableanti-aliasing filter. According to another embodiment, the output of thegyroscopes is digital, in which case the sensor must be configured forthe correct gain and bandwidth and sampled at the appropriate rate to bythe microprocessor 212.

The sensor module 200 also contains one or more aiding sensors.According to one embodiment, an aiding system is a three axismagnetometer 201. By sensing the local magnetic field, the magnetometeris able to record the device's two axes of absolute attitude relative tothe local magnetic field which can aid correcting drift in otherinertial sensors such as the gyroscopes 203. In one embodiment, themagnetometer sensors are off-the-shelf, low noise, solid-state, GMRmagnetometer in small surface-mount packages such as the HoneywellHMC1043. In other embodiments they are custom made MEMS. Themagnetometers are arranged in three orthogonal axes either on a singlemulti-axis device, or by using one or more separate sensors in differentmounting configurations. According to one embodiment, the output of eachmagnetometer 203 is an analog signal from two GMR magnetometers arrangedin a Wheatstone bridge configuration, which requires a differentialoperational amplifier 204 to amplify the signal and an anti-aliasingfilter 207 to remove high frequency components. These amplified,anti-aliased filters are then sampled by the analog-to-digital (ADC)peripheral inputs of the microprocessor 212. According to one embodimentthe anti-aliasing filters are single pole RC low-pass filters thatrequire a high sampling frequency; in another, they are operationalamplifiers with multiple-pole low pass filters that may have a slowersampling frequency. In other embodiments, the device includes an analoginterface circuit (AIC) with a programmable anti-aliasing filter.According to another embodiment, the output of the magnetometers isdigital, in which case the sensor must be configured for the correctgain and bandwidth and sampled at the appropriate rate to by themicroprocessor 212. Unlike conventional MEMS inertial sensors,magnetometer sensors may need considerable support circuitry 208, whichin one embodiment include such functions as temperature compensation ofthe Wheatstone bridge through controlling the bridge current, and lowfrequency magnetic domain toggling to identify offsets through the useof pulsed set/reset coils.

Although not specifically depicted in the sensor module 200, otheraiding sensors could be added. In one embodiment, a Global PositioningSystem Satellite Receiver is added in order to give absolute geodeticposition of the device. In another embodiment, a barometric altimeter isadded to give an absolute indication of the vertical altitude of thedevice. In another embodiment, beacons consisting of devices using thesame wireless transceiver 231 could also tag specific locations byrecording the ID of the beacon.

B.2. Microprocessor Module

The microprocessor module 210 in FIG. 2 is responsible for devicecontrol, device status, as well as local data and communicationprocessing. The microprocessor 212 may indicate the device's status onsome kind of visual or auditory display 211 on the device. In oneembodiment, the display is a a red-green-blue (RGB) light emitting diode(LED). In another embodiment, a small LCD panel is used to displayinformation, such as the time of day, system status such as batterycharge level and data storage level, and a medication reminder forsubjects who require medication for to treat their movement disorder. Inanother embodiment, the medication reminder is a gentle vibration,auditory, or visual cue that reminds subjects to take any necessarytreatment or perform symptom measurement tasks.

According to one embodiment, the microprocessor 212 is a low powermicrocontroller such as the Texas Instruments MSP430FG4618. Themicroprocessor coordinates the sampling of sensors, data processing,data storage, communications, and synchronization across multipledevices. The microprocessor should be a lower power device with enoughcomputational resources (e.g. 20 MIPS) and input/output resources (morethan 20 general purpose input/output lines, 12 analog-to-digitalconverter inputs, and more than two serial communication ports) tointerface to other modules.

The microprocessor is clocked by a low drift time base 213 in order toaccurately maintain both a real time clock (RTC) and to minimize driftin the synchronous sampling across multiple devices on one subject overlong periods of time. In one embodiment, the low drift time base is atemperature compensated crystal oscillator (CTXO) such as the EpsonTG3530SA. In another embodiment, the time base is a standardmicroprocessor crystal with custom temperature compensation using thedigital-to-analog converter of the microprocessor 212. Using a CTXOinstead of a standard microprocessor crystal also minimizes powerconsumed by the wireless communication module 230 since the frequencynecessary to re-synchronize devices is reduced.

B.3. Data Storage Module

The data storage module 221 stores the measurements from the sensors 200and status of the device (such as the energy storage device's 245 chargelevel) locally on the device. It is especially designed to supportstudies involving multi-day continuous movement monitoring. In oneembodiment, the device is capable of storing movement data at a samplingfrequency of 128 Hz for over 20 days. In one embodiment, the localstorage is flash memory soldered to the device's printed circuit board.In another embodiment, a high capacity Flash card, such as a >4 GBMicroSD card, is used with a high speed synchronous serial port (SPI)from the microprocessor 212 to minimize wire complexity and to enable astandard protocol to hand off to a host computer as necessary. Inanother embodiment, the data storage module is greatly reduced, or evenunnecessary, because data is streamed directly off the device using thewireless communication module 230.

B.4. Wireless Communication Module

The wireless communication module 230 allows the device to communicateto other devices (peer-to-peer), to a host computer (peer-to-host) andto listen to other data such as wireless beacons. The wirelesscommunication module serves multiple functions: it broadcasts data fromthe device's inertial sensors 200 to a computer or other recordingdevice, it synchronizes sampling rate across multiple devices through asampling time synchronization protocol, and allows for configuring thedevices behavior (i.e. mode of operation). Another use for the wirelesscommunication module is to listen for transmissions from beacons whichinforms the device about its current location (e.g. bathroom, kitchen,car, workplace). In one embodiment, the communication protocol is aindustry standard protocol such as Bluetooth, ZigBEE, WiFi orsubstantially equivalent protocol. In another embodiment, it is a customcommunication protocol based on a physical layer transceiver chip.

One embodiment of the wireless communication module consists of a lowpower, 2.4 GHz surface mount wireless transceiver 231, such as theNordic Semiconductor nRF24L01+. The wireless transceiver uses a smallon-board antenna 232, such as a chip antenna like the gigaNOVA Micaantenna for both transmitting and receiving wireless communications. Inanother embodiment, the antenna is a groundplane PCB patch antenna. Inone embodiment, the wireless transceiver 231 uses a high speedsynchronous serial port, such as the serial peripheral interface (SPI),to communicate with the host microprocessor 212. In another embodiment,the wireless transceiver is built into the microprocessor as aperipheral. In another embodiment, the wireless transceiver uses skinconduction to create a Personal Area Network (PAN) instead of abroadcast radio. Another embodiment uses light, such as infrared light,as a wireless communication system like the industry standard IRDA. Inthis last embodiment, the antenna 232 would be an optical transceiver.

B.5. Wireless Synchronization B.5.A. Master Synchronization Scheme

According to one embodiment the movement monitor incorporates a wirelesssynchronization scheme based on master synchronization. In the masterwireless synchronization scheme a plurality of movement monitors on awireless network with a plurality of access points receive the datagenerated by the wireless network. One of these access points, which isidentified during configuration, becomes the master timing source forthe entire network. All other access points are synchronized to themaster. FIG. 3 illustrates a block diagram representing an embodiment ofa wireless synchronization scheme based on a single master clock.

In one embodiment, the access points are synchronized to the masterusing a cable to transmit a synchronization clock. In anotherembodiment, the between-access point synchronization signal is sent overthe wireless network between access points, possibly on a differentwireless channel. In another embodiment, the synchronization signal issent from the master access point to the other access points viaconnection to a local host computer.

The access point synchronization signal is used to precisely time thetransmission of a synchronization data packet. This data packet istransmitted at the exact same time by all access points and is receivedby all wireless nodes. This synchronized packet, in one embodiment,contains the counter value representing the time since the epoch for themaster access point clock.

On receipt of the synchronization data packet, the wireless nodes adjusttheir clock or primary timer based on their local time stamp of thereception of that packet. In one embodiment, the nodes utilize atimer-based hardware capture (capture and compare) input pin to get aprecise offset between the arrival of the synchronization packet and thedevice's local time. This offset can be used to measure the drift in thesensor node's clock and allow the node to either adjust its clockfrequency directly via a voltage controlled oscillator, or allow it toperiodically adjust a counter/timer to be used for sampling.

According to a particular embodiment, and without limitation, a singleaccess point is chosen to be the master access point, and thus themaster clock, for the entire wireless network. At the same time, allaccess points are updated to the same 64 bit absolute time stamp. Thisaccess point generates a precisely and deterministically timed clocksignal using its PWM peripheral which is distributed to all other accesspoints. On receipt of the clock pulse, each access point enters a highpriority interrupt which has a known, deterministic delay to execution.Then each access point executes a predetermined number of instructionsto send a synchronization packet from the access points to the rest ofthe wireless sensor nodes. This synch packet includes the absolute time.The radios on the wireless sensor nodes receive the packet and assert aninterrupt line. This interrupt line is tied to a capture and compareperipheral pin, which takes a snapshot of the local timer in aninterrupt. This snapshot allows the sensor node to reliably anddeterministically find out when exactly the packet was sent according toits onboard time base. The sensor node takes this snapshot and comparesit to what it should be, given a known synchronization packet rate. Thedifference is used in a simple software PLL to synchronize the localtimer with the master access point clock.

The advantage to the master synchronization scheme is that it allows thesensor nodes to quickly and easily come into synchronization with thenetwork: it requires very little computation to adjust the local clockson the nodes, and the isochronous rate of the synchronization packetscan be adjusted based on the need for synchronization tolerance. Thehigher the rate, the less time there is for clock drift.

FIG. 15 illustrates the use of the complete system according to oneembodiment where wireless master or mesh synchronized data is collectedduring continuous monitoring by the movement monitors and stored locallyuntil the monitors are docketed and the docking station transfers thedata to a computer system including analysis methods to visualize andproduce reports of the results.

B.5.B. Mesh Synchronization Scheme

According to an alternative embodiment the wireless synchronizationscheme is comprised of a plurality of sensors on a wireless network witha plurality of access points to receive the data generated by thewireless network. In this scheme, however, there is no master timesource. Instead, each device on the network sends a synchronizationpacket during its prescribed time slot, enabling each device to compareits clock against the clock of each of the other nodes and access pointsin the wireless network. This comparison allows each node in the mesh tocreate a statistical model of the network time—a distributed statisticalclock model—and of its own clock relative to the network time. FIG. 4illustrates a block diagram representing an embodiment of a wirelesssynchronization scheme based on mesh synchronization.

Packet transmission and reception in the mesh synchronization schememust be deterministic. In one embodiment, the sending and receiving ofmesh synchronization packets is tied to a transmit enable from a localhardware timer. The packets will be sent at the exact time according tothe local clock, and on receiving the synchronization packets, the nodeswill capture their local timer values to determine their relativeoffsets.

In one embodiment, and without limitation, the Flooding TimeSynchronization Protocol (FTSP) is used to synchronize the nodes. FIG. 5illustrates a block diagram representing an embodiment of a wirelesssynchronization scheme based on mesh synchronization using the FloodingTime Synchronization Protocol (FTSP). A single node is dynamicallyelected to maintain global time. All other nodes synchronize theirclocks to that of this root node. Each node receives synchronizationpackets from the root node and uses them to build a linear regressionmodel of offset and drift from the global time. Once synchronized, thesenodes can broadcast synchronization packets for nodes which are out ofrange of the root node to use for synchronization. According to oneparticular embodiment, the FTSP protocol uses two-way messaging to dosender-receiver synchronization propagating out from a root node. Thefirst step in the FTSP mesh synchronization is to dynamically choose aroot node. After waiting for the timeout period, ROOT-TIMEOUT, withoutreceiving a synchronization packet each node will declare itself rootand start sending out synchronization packets. Upon receiving asynchronization packet from another node, if that node's device ID islower than a device that has declared itself root, it demotes itself toa normal node. In this way, the node with the lowest device ID willeventually be the only root node. Each time a synchronization packet isreceived, the node checks to see if it is a root. If it is a root, thenit checks to see if its device ID is less than the packet's root ID. Ifthe device ID is less, nothing happens and this node stays a root. Ifthe device ID is greater, this node stops being a root, and uses thepacket's root ID for any future synchronization packets it sends out.Whenever a regular node receives a synchronization packet, it calculatesthe difference between the packet's global time and the local time. Thisdifference is shifted into a buffer for linear regression. If theregression buffer is full, the linear regression is calculated. Thelinear regression produces an offset and drift estimate. The device isnow considered synchronized and can transmit its own synchronizationpackets with the root ID and the corrected local time whenever it gets anew packet. Each synchronization packet contains the current global timeaccording to the transmitter, the root device ID, and thesynchronization packet count. The packet counter is incremented by theroot every time a new packet is sent. When a regular node sends a packetit uses the most recent packet count it has received.

In another embodiment, the FTSP is modified such that each synchronizednode broadcasts its estimated clock model parameters. The root node canthen estimate it's own parameters such that the error of all the clocksfrom the nominal frequency is minimized. If the distribution of clockfrequencies is centered about the nominal frequency, this will reducedrift with respect to actual time. In another embodiment, the ReferenceBroadcast Protocol is used to synchronize the nodes. A root node ischosen to send synchronization packets. The other nodes then exchangetheir local times upon receipt of each synchronization packet. Inanother embodiment, the Timing-sync Protocol for Sensor Networks isused.

In another embodiment, each node in the network will calculateconfidence intervals for its own clock and provide this to other nodesfor use in calculating the weight that its clock should provide to thestatistical network time. In another embodiment, each node calculatesthe confidence interval for the other nodes based on the variance ofreceived packet time compared to their local clock.

In cases where a node or subset of nodes gets disconnected from thenetwork, they will calculate their own network time using the nodes theycan connect to. The larger the network, or the better their local clock,the more confident the unified network time can be. In the case wheretwo or more groups are connected via a small subset of nodes the unifiedtime can be propagated throughout the network. When two or more subsetsof the network get completely disconnected from each other the chancefor multiple diverging network times can occur. Reconnection of the twosubnets is smoothly implemented by using the statistical modeling andallowing only very slow slewing of local clocks.

FIG. 14 illustrates the use of the complete system according to oneembodiment where wireless mesh synchronized data is collected duringcontinuous ambulatory monitoring by the movement monitors and storedlocally until the monitors are docketed and the docking stationtransfers the data to a computer system including analysis methods tovisualize and produce reports of the results.

B.6. Robust Wireless Data Transfer Controller

FIG. 6 illustrates a block diagram representing the basic components ofan embodiment of the general systems for robust wireless communicationsin small wireless systems including a data collection unit 600, a datacontroller unit 602, a data storage unit 608, a radio 604, and anantenna 606. Disclosed embodiments include a new apparatus for robustwireless communications for small wireless systems, such as a wearablemovement monitor, comprising of (a) a small sized, large capacity, lowpower, nonvolatile data storage unit, (b) a low power wirelesscommunication system, (c) a small antenna, (d) a data collection unit tocollect data to be transmitted, (e) a data controller to control theflow and storage of data in the system, and (f) data controller means tocontrol how the data is processed, stored and transmitted. The datastorage unit is a small sized, large capacity, low power, nonvolatiledata storage system. In one embodiment, and without limitation, it is acommercially available microSD card with 8 GB of data storage. Inanother embodiment, it is a large capacity Flash surface-mounted IC. Inanother embodiment, it is a large capacity SDRAM chip with batterybackup.

The low power radio unit is a small volume, extremely low power radiosystem. In one embodiment, it is a a Nordic Semiconductor nRF24L01+2.4GHz transceiver. In another embodiment, it is a low power IC thatconforms to a radio standard such as Bluetooth or IEEE 802.15 (ZigBee).The small antenna is an extremely small volume antenna that trades areduction in radiation efficiency for an decrease in the occupied volumeby the antenna. In one embodiment, the antenna is a small custom made2.4 GHz PCB patch antenna. In another embodiment, it is a commerciallyavailable chip antenna. The data collection unit collects the data to betransmitted. In one embodiment, the data collection unit is asix-degree-of-freedom inertial measurement unit (three axisaccelerometers, three axis gyroscopes). In another embodiment, the datacollection unit contains a six-degree-of-freedom inertial measurementunit (three axis accelerometers, three axis gyroscopes), a three axismagnetometer, and a temperature sensor. The data controller controls theflow of data from the data collection unit to the data storage unit, andfrom the data storage unit to the low power radio unit. In oneembodiment the data controller is a microcontroller such as the TexasInstruments MSP430FG4618, in another it is a programmable logic devicelike an FPGA or CPLD.

In order to achieve robust wireless data transfer the system andapparatus includes a data transfer controller 602 that can run one ofseveral methods, optimizing for power, communication bandwidth, orrobustness. In one embodiment, the data controller methods running onthe data controller store all data from the data collection on the datastorage unit, and stream the data from the data storage unit to the lowpower radio unit as the unreliable radio channel allows.

In another embodiment, the data controller method first sends the datato the lower power radio unit, then stores only the data that has failedto successfully transmit.

In another embodiment, the data controller methods store data in thedata storage unit while sensing that the state of the communicationchannel. If the channel is not available, the data controller methodsshuts off the low power radio to save power, and continues to poll thechannel until it is available.

In another embodiment, the data controller methods store the data in thedata storage unit, and only occasionally turns on the radio into theirfull speed modes in order to quickly and efficiently “burst” the datafrom the device.

In another embodiment, the external data storage unit utilizes a singledata bus with only half duplex reads and writes. In this case, the datacontroller methods must schedule and prioritize the data on the databus. In the case where sensor data is being produced at a constant ratethere is a hard real time requirement that writes take precedence overreads to prevent the loss of data. It is therefore possible for theradio unit to be temporarily starved of data pending a read requestsince a pending read operation is only performed if there are no pendingwrites in the queue.

In another embodiment, the data controller has a “data latency bound”that enables the data controller methods to keep only so many seconds(or minutes, or hours) of data before discarding the data.

FIG. 11 illustrates an embodiment of the access point. FIG. 16illustrates the use of the complete system according to one embodimentwhere wireless mesh synchronized data is collected during continuous orobjective monitoring by the movement monitors and such data iswirelessly streamed using robust wireless streaming to a computer systemincluding analysis methods to visualize and produce reports of theresults.

B.7. Power and Docking Module

The power and docking module 240 provides external power, powerregulation, and external data connections to the device. One aspect ofthe power and docking module is the docking connector 242 which providesan external connector to access external power and provide high speedcommunication with the docking station, and thus to a computer or otherrecording device. One embodiment of the connector 242 is the Hirose ST60series connector which provides enough connections for both power andcomplete hand off of the data storage module 220 for extremely highthroughput downloading of data. In another embodiment, the dockingconnector is completely wireless, and provides inductive wireless powertransmission for external power and a local high speed wireless datachannel.

Most energy storage devices much be carefully charged, so the energystorage charging regulator 244 must carefully charge the energy storagedevice 245. In one embodiment, the energy storage charger is a linearLithium Ion Polymer battery charger IC such as the Microchip MCP73833,or substantially equivalent integrated circuit. In another embodiment,it is a switching battery charge IC. In another embodiment, themicroprocessor 212 measures the battery capacity and controls the energystorage device's charge directly.

The energy storage mechanism 245 is in one embodiment a Lithium IonPolymer battery.

Other embodiments involve other energy storage mechanism, such as supercapacitors or other battery chemistries. The Lithium ion polymer batteryshould be sized appropriately to be as small as possible for the comfortof the subject wearing the device, yet still contain enough storedenergy to power the system for a sufficiently long period of time. Inone embodiment, a 450 mAHr battery is used to enable the device to last24 hours and thus be usable for a full day before recharging isrequired. In another embodiment, a smaller 50 mAHr battery is used tominimize the device size for short term clinical use.

A power regulator 243 must be used to regulate the power coming from theenergy storage device. According to one embodiment, a simple voltageregulator such as the Texas Instruments TPS79901 or equivalent, preparesthe energy storage device's power for use by the other modules (200,210, 210, 220, 230).

Device operation can be extended or performance improved by harvestingenergy from the local environment. One embodiment of an energyharvesting device 241 is a small solar panel on the outside of thedevice. Another is a small kinetic generator using piezoelectricmaterials to generate voltage. A third uses heat differences between thesubject's skin and the ambient air temperature.

B.8. External Docking Station

According to one embodiment, in order to facilitate use in the clinic,home, or other normal daily environments, the device includes a dockingstation 102 that is used to charge the batteries of the wearable devices100 and download the data from each day of activities. The dockingstation 102 uploads the data using whatever means is available in thatsetting. If high-speed Internet access is available within the home,this may be used for data upload. Alternatively it permits the user todownload the data to a portable storage device such as a USB thumb driveor hard drive that can then be transported to a site for final upload tothe data server. If there is no simple means to download the data fromthe docking station 102, the data is downloaded once the docking stationis returned at the end of the monitoring period. The docking station 102requires no user intervention. The devices 100 stop recording as soon asthey are docked and start recording as soon as they are undocked.According to one embodiment, the docking station 102 does not includeany buttons. The docking station 102 can be connected to a computer fordata extraction and processing.

FIG. 7 illustrates a particular embodiment of the movement monitor, thedocking station, and the docking mechanism. FIG. 8 illustrates a secondembodiment of the movement monitor, the docking station, and the dockingmechanism, this embodiment particularly adapted to the wearable a wristwatch. FIG. 10 illustrates an embodiment of the docking station and aconnected docking station for simultaneously charging multiple movementmonitors.

B.9. Clinical Data Management and Processing Module

Once the data is uploaded to the server 104 including a clinical datamanagement tool, the server 104 runs automatic statistical signalprocessing methods 106 to analyze the data and compute the resultsneeded for the application. According to one embodiment, the systemprovides data for three applications: 1) human movement research, 2)movement disorders studies and clinical trials, and 3) clinical care.The system provides a simple means for researchers to conduct studies inhuman movement with wearable sensors 100. Study participants have aneasy means of handling the devices by simply docking them when not inuse. Researchers have easy, secure, and protected access to their rawsensor data through the server 104. The system also provides fullsupport for research studies and clinical trials in movement disorderssuch as Parkinson's disease and essential tremor. It permits researchersto easily upload other types of data such as clinical rating scalescores, participant information, and other types of device dataintegrated into a secure database, and provides a means for sharing thedata. Different views and controlled access permit study coordinators,research sponsors, statisticians, algorithm developers, andinvestigators to easily monitor the progress of studies and results. Thesystem also provides the ability to do sequential analysis forcontinuous monitoring of clinical studies. According to one embodiment,the system has strict, secure, and encrypted access to any protectedhealth information that is stored in the server. The system alsosupports clinical monitoring of individual patients to determine theirresponse to therapy. This is especially helpful for movement disorderssuch as advanced Parkinson's in which the degree of motor impairmentfluctuates continuously throughout the day. As with clinical studies andtrials, the server provides secure, encrypted access to patient recordsfor authenticated care providers as well as patients themselves.

According to one embodiment, the algorithms 106 process the raw devicedata and extract the metrics of interest. These algorithms areinsensitive to normal voluntary activities, but provide sensitivemeasures of the motor impairments of interest. In Parkinson's diseasethis may include tremor, gait, balance, dyskinesia, bradykinesia,rigidity, and overall motor state.

Certain specific details are set forth in the above description andfigures to provide a thorough understanding of various embodimentsdisclosed. Certain well-known details often associated with computing,firmware, and software technology are not set forth in the followingdisclosure to avoid unnecessarily obscuring the various disclosedembodiments. Further, those of ordinary skill in the relevant art willunderstand that they can practice other embodiments without one or moreof the details described below. Aspects of the disclosed embodiments maybe implemented in the general context of computer-executableinstructions, such as program modules, being executed by a computer,computer server, or device containing a processor. Generally, programmodules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Aspects of the disclosed embodiments may also bepracticed in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote storage media includingmemory storage devices. Those skilled in the art will appreciate that,given the description of the modules comprising the disclosedembodiments provided in this specification, it is a routine matter toprovide working systems which will work on a variety of known andcommonly available technologies capable of incorporating the featuresdescribed herein.

While particular embodiments have been described, it is understood that,after learning the teachings contained in this disclosure, modificationsand generalizations will be apparent to those skilled in the art withoutdeparting from the spirit of the disclosed embodiments. It is noted thatthe foregoing embodiments and examples have been provided merely for thepurpose of explanation and are in no way to be construed as limiting.While the system has been described with reference to variousembodiments, it is understood that the words that have been used hereinare words of description and illustration, rather than words oflimitation. Further, although the system has been described herein withreference to particular means, materials and embodiments, the actualembodiments are not intended to be limited to the particulars disclosedherein; rather, the system extends to all functionally equivalentstructures, methods and uses, such as are within the scope of theappended claims. Those skilled in the art, having the benefit of theteachings of this specification, may effect numerous modificationsthereto and changes may be made without departing from the scope andspirit of the disclosed embodiments in its aspects.

1. A movement monitoring apparatus comprising a wireless synchronizationscheme.
 2. The movement monitoring apparatus of claim 1, wherein saidwireless synchronization scheme is a master synchronization scheme or amesh synchronization scheme.
 3. The movement monitoring apparatus ofclaim 2, further comprising a robust wireless data transfer datacontroller.
 4. The movement monitoring apparatus of claim 3, whereinsaid mesh synchronization scheme includes a statistical model of anetwork time and of said movement monitor apparatus relative to saidnetwork time.
 5. The wearable movement monitoring apparatus of claim 4,wherein said statistical model of the network time is a distributedstatistical clock model.
 6. The wearable movement monitoring apparatusof claim 5, wherein said mesh synchronization scheme is based on aflooding time synchronization protocol.
 7. The wearable movementmonitoring apparatus of claim 6, wherein said flooding timesynchronization scheme is adapted to enable said movement monitoringapparatus to broadcasts its estimated clock model parameters.
 8. Thewearable movement monitoring apparatus of claim 7, wherein said robustwireless data transfer data controller stores data packets locally if atransmission error is detected, and re-transmits said data packets at alater point in time, whereby no data packets are lost during wirelessdata transfers.